This paper presents a systematic evaluation of four state-of-the-art BioNER models (BioBERT, PubMedBERT, AIONER, and VANER) across five benchmark datasets covering diseases, genes, proteins, species, and chemical compounds. Although transformer-based architectures have advanced BioNER, direct comparisons remain difficult due to inconsistent annotation guidelines, tokenization schemes, and a lack of standardized benchmarks. To address these issues, we propose a unified pipeline that includes consistent data preprocessing, uniform model fine-tuning, and performance assessment using F1-score. Our results indicate that BioBERT and PubMedBERT often excel, reflecting the benefits of domain-specific pretraining, while AIONER performs competitively through structured sequence modeling and VANER shows variability despite excelling in taxonomic and chemical domains. Beyond model comparisons, we underscore practical considerations—such as ease of implementation, adaptability to novel entities, and resource requirements—and release all code, model configurations, and preprocessed datasets to foster reproducibility and further research.